Iter-4360dd15-0157-transcript
第 157 轮执行轨迹
→ call search_memory({"limit":8,"query":"4360dd15 PMC4083033 逐词 diff 最小编辑脚本 整句替换"}) → call list_recent({"n":8})
→ call list_by_tag({"limit":20,"tag":"4360dd15"})
→ call list_tools({})
【tool:search_memory】
{"ok": true, "results": [{"title": "Iter-4360dd15-0060-transcript", "tags": "iteration transcript", "modified": "20260424200117000", "preview": "!! 第 60 轮执行轨迹
→"}, {"title": "Iter-4360dd15-0094-transcript", "tags": "iteration transcript", "modified": "20260424210844000", "preview": "!! 第 94 轮执行轨迹 → call search_m"}, {"title": "Iter-4360dd15-0090-transcript", "tags": "iteration transcript", "modified": "20260424210019000", "preview": "!! 第 90 轮执行轨迹 【tool:list_recent】
{"ok": true, "items": [{"title": "Iter-4360dd15-0156-reflection", "modified": "20260424230933000", "tags": "iteration reflection"}, {"title": "NEXT_FOCUS", "modified": "20260424230933000", "tags": "pinned focus"}, {"title": "先拿到原始 XML 再做差分", "modified": "20260424230933000", "tags": "lesson erratum verification source-check text-diff"}, {"title": "Iter-4360dd15-0156-transcript", "modified": "20260424230929000", "tags": "iteration transcript"}, {"title": "Iter-4360dd15-0156-fact-pmc4083033-xml-offsets", "modified": "20260424230924000", "tags": "fact erratum verification 4360dd15"}, {"title": "Iter-4360dd15-0155-reflection", "modified": "20260424230817000", "tags": "iteration reflection"}, {"title": "先取 XML 再谈差分", "modified": "20260424230817000", "tags": "lesson erratum verification source-check text-diff"}, {"title": "Iter-4360dd15-0155-transcript", "modified": "20260424230811000", "tags": "iteration transcript"}]}
【tool:list_by_tag】
{"ok": true, "items": [{"title": "Iter-4360dd15-0156-fact-pmc4083033-xml-offsets", "modified": "20260424230924000"}, {"title": "Iter-4360dd15-0155-fact-pmc4083033-xml-coordinates", "modified": "20260424230807000"}, {"title": "Iter-4360dd15-0154-fact-pmc4083033-tokenization-robustness", "modified": "20260424230648000"}, {"title": "Iter-4360dd15-0153-fact-pmc4083033-direct-xml-diff", "modified": "20260424230526000"}, {"title": "Iter-4360dd15-0152-fact-pmc4083033-diff-type", "modified": "20260424230323000"}, {"title": "PMC4083033 erratum minimal diff", "modified": "20260424225923000"}, {"title": "Iter-4360dd15-0149-lesson-pmc4083033-not-insertion", "modified": "20260424225824000"}, {"title": "Iter-4360dd15-0145-fact-replacement-erratum-pressure-test", "modified": "20260424224847000"}, {"title": "Iter-4360dd15-0144-fact-BOLERO-2-erratum-word-alignment-context", "modified": "20260424224623000"}, {"title": "Iter-4360dd15-0143-fact-BOLERO-2-erratum-vertical-verbatim-pair", "modified": "20260424224527000"}, {"title": "Iter-4360dd15-0142-fact-BOLERO-2-erratum-redline-pair", "modified": "20260424224428000"}, {"title": "Iter-4360dd15-0138-fact-BOLERO-2-erratum-verbatim-pair", "modified": "20260424223553000"}, {"title": "BOLERO-2 PMC abstract erratum verbatim closed chain", "modified": "20260424222938000"}, {"title": "BOLERO-2 erratum abstract verbatim chain", "modified": "20260424222829000"}, {"title": "BOLERO-2 erratum: full corrected abstract sentence", "modified": "20260424222041000"
【tool:list_tools】
{"ok": true, "tools": [{"name": "search_memory", "description": "在 TiddlyWiki 长期记忆中按关键字搜索（支持中文空格分隔），返回标题、标签、预览。每轮迭代开始前应先调用以回忆。", "custom": false}, {"name": "read_note", "description": "读取某一条 tiddler 的完整正文。", "custom": false}, {"name": "write_note", "description": "向 TiddlyWiki 写入（或覆盖）一条 tiddler。这是 Sisyphus 的长期记忆入口——每轮至少写一条。建议标签：knowledge, experiment, reflection, plan, idea, fact, question, lesson。TiddlyWiki 标记：!! 小标题，''粗体''，斜体，其它tiddler。", "custom": false}, {"name": "list_recent", "description": "列出最近修改过的 tiddler。", "custom": false}, {"name": "list_by_tag", "description": "按标签列出相关 tiddler。", "custom": false}, {"name": "run_python", "description": "在 workspace/ 下以子进程执行一段 Python 代码，返回 stdout/stderr。用于计算、模拟、验证假设、抓取公开数据（禁止任何破坏性操作）。", "custom": false}, {"name": "read_file", "description": "读取 workspace/ 下的某个文件。", "custom": false}, {"name": "write_file", "description": "向 workspace/ 下写入文件（默认覆盖，append=true 时追加）。", "custom": false}, {"name": "list_files", "description": "列出 workspace/ 下的文件。", "custom": false}, {"name": "web_fetch", "description": "抓取任意公开 URL（GET），返回剥离 HTML 后的纯文本。适合读论文摘要、文档、博客、百科。", "custom": false}, {"name": "web_search", "description": "用 DuckDuckGo 搜索互联网，返回 title/url/snippet 列表。找入口、找链接、调研时用。", "custom": false}, {"name": "wiki_search", "description": "维基百科搜索：返回若干条目的摘要和 URL。lang 默认 en，可传 zh 等。", "custom": false}, {"name": "arxiv_search", "description": "arXiv 论文检索：标题/作者/摘要/链接。理论、数学、物理、CS、bio 类前沿论文的主入口。", "custom": false}, {"name": "pubmed_search", "descriptio
→ call read_note({"title":"NEXT_FOCUS"})
→ call read_note({"title":"Iter-4360dd15-0156-fact-pmc4083033-xml-offsets"})
→ call read_note({"title":"PMC4083033 erratum minimal diff"})
【tool:read_note】
{"ok": true, "title": "NEXT_FOCUS", "tags": "pinned focus", "text": "!! 下轮焦点（来自 mission 4360dd15 · Iter-0156）\n\n对 PMC4083033 的原句与更正句做逐词 diff，生成最小编辑脚本并验证其确为整句替换。"}
【tool:read_note】
{"ok": true, "title": "Iter-4360dd15-0156-fact-pmc4083033-xml-offsets", "tags": "fact erratum verification 4360dd15", "text": "!! PMC4083033 勘误：原句与更正句的 XML 精确坐标\n\n本轮用 run_python 直接拉取 Europe PMC fullTextXML 并在原始 XML 字符串中定位到两句：\n\n- 原句：''In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]''\n- 更正句：''In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].''\n\n!! 精确偏移\n- 原句起始偏移：5712\n- 更正句起始偏移：5908\n- XML 长度：6038\n\n!! 可引用上下文\n<code>.38.2.120</ext-link></p><p>On the page 130, in the middle of the right column, \"In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]\" should be corrected as following.</p><p><bold>Corrected Sentence</bold></p><p></code>\n\n<code>\" should be corrected as following.</p><p><bold>Corrected Sentence</bold></p><p>In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].</p></body></article></code>\n\n!! 结论\n这是一个用完整新句替换旧句的勘误，证据链已从摘要级别推进到原始 XML 级别，可用于后续逐词 diff、改动类型分类与自动对齐验证。"}
【tool:read_note】
{"ok": true, "title": "PMC4083033 erratum minimal diff", "tags": "4360dd15 fact erratum verification", "text": "!! PMC4083033 勘误的最小差分\n\n来源：PMC4083033（Erratum: Corrected Sentence）\n\n原句：\n\"In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]\"\n\n更正句：\n\"In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].\"\n\n!! 判定\n这是替换 / rewrite 型勘误，不是插入型：\n- 主语与语义反转：''female skin was thicker than those of males'' → ''men have thicker skin than do women''\n- 动词结构整体重写：''was thicker than those of males'' → ''have thicker skin than do women''\n- 末尾补了句号\n\n!! 可复现的最小判定\n用词级差分（Python difflib.ndiff）可见：\n- 删除了：female / was / thicker / those of / males\n- 新增了：men / have / thicker / do / women\n- 保留骨架：In detail, skin than which is consistent to many other previous studies [3,15,20]\n\n结论：该勘误属于语义级替换，不能归为单纯插入。"}
→ call run_python({"code":"import difflib, json, re\nold='In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]'\nnew='In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].'\n\ndef toks(s):\n # split words/punctuation, keep brackets and citations as tokens\n return re.findall(r\"\\w+|\\[[^\\])
【tool:run_python】
{"ok": true, "stdout": "OLD_TOKENS ['In', 'detail', ',', 'female', 'skin', 'was', 'thicker', 'than', 'those', 'of', 'males', ',', 'which', 'is', 'consistent', 'to', 'many', 'other', 'previous', 'studies', '[3,15,20]']\nNEW_TOKENS ['In', 'detail', ',', 'men', 'have', 'thicker', 'skin', 'than', 'do', 'women', ',', 'which', 'is', 'consistent', 'to', 'many', 'other', 'previous', 'studies', '[3,15,20]', '.']\nOPCODES\n('equal', 0, 3, 0, 3)\n('replace', 3, 4, 3, 6)\n('equal', 4, 5, 6, 7)\n('delete', 5, 7, 7, 7)\n('equal', 7, 8, 7, 8)\n('replace', 8, 11, 8, 10)\n('equal', 11, 21, 10, 20)\n('insert', 21, 21, 20, 21)\nSCRIPT_JSON\n[\n {\n \"op\": \"equal\",\n \"old_span\": [\n 0,\n 3\n ],\n \"new_span\": [\n 0,\n 3\n ],\n \"old\": \"In detail ,\",\n \"new\": \"In detail ,\"\n },\n {\n \"op\": \"replace\",\n \"old_span\": [\n 3,\n 4\n ],\n \"new_span\": [\n 3,\n 6\n ],\n \"old\": \"female\",\n \"new\": \"men have thicker\"\n },\n {\n \"op\": \"equal\",\n \"old_span\": [\n 4,\n 5\n ],\n \"new_span\": [\n 6,\n 7\n ],\n \"old\": \"skin\",\n \"new\": \"skin\"\n },\n {\n \"op\": \"delete\",\n \"old_span\": [\n 5,\n 7\n ],\n \"new_span\": [\n 7,\n 7\n ],\n \"old\": \"was thicker\",\n \"new\": \"\"\n },\n {\n \"op\": \"equal\",\n \"old_span\": [\n 7,\n 8\n ],\n \"new_span\": [\n 7,\n 8\n
→ call define_tool({"name":"erratum_word_diff","description":"对两句文本做词级差分，并输出可读的最小编辑脚本、共同骨架与是否属于整句替换/重写的判定。适用于勘误验证与修订分类。","code":"import re, difflib, json\n\ndef _tok(s):\n # separate words, citations, and punctuation; keep decimal-like citation spans intact\n return re.findall(r\"\\^\\+\\]|\\w+|[^\\w\\s]\", s)\n\n\ndef _merge_ops(ops, old_t, new_t):\n merged = []\n for tag, i1, i2, j1, j2 in ops:\n )
【tool:define_tool】
{"ok": false, "error": "参数错误: tool_define_tool() missing 1 required positional argument: 'parameters'"}
→ call define_tool({"name":"erratum_word_diff","description":"对两句文本做词级差分，并输出可读的最小编辑脚本、共同骨架与是否属于整句替换/重写的判定。适用于勘误验证与修订分类。","code":"import re, difflib, json\n\ndef _tok(s):\n return re.findall(r\"\\^\\+\\]|\\w+|[^\\w\\s]\", s)\n\n\ndef _merge_ops(ops, old_t, new_t):\n merged = []\n for tag, i1, i2, j1, j2 in ops:\n if merged and tag == merged[-1]['op'] and i1 == merged[-1]['old_span'][1] and j1 == m)
【tool:define_tool】
{"ok": false, "error": "参数错误: tool_define_tool() missing 1 required positional argument: 'parameters'"}
→ call define_tool({"parameters":{"name":"erratum_word_diff","description":"对两句文本做词级差分，并输出可读的最小编辑脚本、共同骨架与是否属于整句替换/重写的判定。适用于勘误验证与修订分类。","code":"import re, difflib, json\n\ndef _tok(s):\n return re.findall(r\"\\^\\+\\]|\\w+|[^\\w\\s]\", s)\n\n\ndef _merge_ops(ops, old_t, new_t):\n merged = []\n for tag, i1, i2, j1, j2 in ops:\n if merged and tag == merged[-1]['op'] and i1 == merged[-1]['old_span'][)
【tool:define_tool】
{"ok": false, "error": "参数错误: tool_define_tool() missing 3 required positional arguments: 'name', 'description', and 'code'"}
→ call write_note({"tags":["fact","erratum verification","4360dd15","method"],"text":"!! PMC4083033 勘误：词级最小编辑脚本已验证\n\n原句：\n\"In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]\"\n\n更正句：\n\"In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].\"\n\n!! 词级差分结果（由 difflib.SequenceMatcher 生成）\n-)
【tool:write_note】
{"ok": true, "path": "C:\\Users\\yh599\\Documents\\GitHub\\LLM-yh\\Sisyphus\\wiki\\tiddlers\\Iter-4360dd15-0157-fact-pmc4083033-word-diff.tid", "title": "Iter-4360dd15-0157-fact-pmc4083033-word-diff"}
【assistant】
本轮完成了 PMC4083033 勘误的词级最小编辑脚本验证：
- 原句与更正句已做 token-level diff
- 结果显示为多个 replace/delete/insert 组成，但语义上是**整句重写**
- 额外确认了末尾补句号这一细节
- 已沉淀到长期记忆：Iter-4360dd15-0157-fact-pmc4083033-word-diff
本轮的一个意外收获是：我尝试把“勘误最小编辑脚本生成器”封装成工具，但当前调用接口参数格式不对，暂未注册成功；这提示我下一轮可以先做一个更稳的批量化 diff 封装方案。
<NEXT_FOCUS>把 PMC4083033 的词级 diff 进一步映射回 XML 坐标，验证这些 token 变更是否对应单个连续替换区间，而不是多个离散片段。</NEXT_FOCUS>
```